data <- read_csv("diabetes.csv")
## Rows: 768 Columns: 9
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## dbl (9): Pregnancies, Glucose, BloodPressure, SkinThickness, Insulin, BMI, D...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
colnames(data)
## [1] "Pregnancies"              "Glucose"                 
## [3] "BloodPressure"            "SkinThickness"           
## [5] "Insulin"                  "BMI"                     
## [7] "DiabetesPedigreeFunction" "Age"                     
## [9] "Outcome"
data
## # A tibble: 768 × 9
##    Pregnancies Glucose BloodPressure SkinThickness Insulin   BMI
##          <dbl>   <dbl>         <dbl>         <dbl>   <dbl> <dbl>
##  1           6     148            72            35       0  33.6
##  2           1      85            66            29       0  26.6
##  3           8     183            64             0       0  23.3
##  4           1      89            66            23      94  28.1
##  5           0     137            40            35     168  43.1
##  6           5     116            74             0       0  25.6
##  7           3      78            50            32      88  31  
##  8          10     115             0             0       0  35.3
##  9           2     197            70            45     543  30.5
## 10           8     125            96             0       0   0  
## # ℹ 758 more rows
## # ℹ 3 more variables: DiabetesPedigreeFunction <dbl>, Age <dbl>, Outcome <dbl>
test <- na.omit(data)
test
## # A tibble: 768 × 9
##    Pregnancies Glucose BloodPressure SkinThickness Insulin   BMI
##          <dbl>   <dbl>         <dbl>         <dbl>   <dbl> <dbl>
##  1           6     148            72            35       0  33.6
##  2           1      85            66            29       0  26.6
##  3           8     183            64             0       0  23.3
##  4           1      89            66            23      94  28.1
##  5           0     137            40            35     168  43.1
##  6           5     116            74             0       0  25.6
##  7           3      78            50            32      88  31  
##  8          10     115             0             0       0  35.3
##  9           2     197            70            45     543  30.5
## 10           8     125            96             0       0   0  
## # ℹ 758 more rows
## # ℹ 3 more variables: DiabetesPedigreeFunction <dbl>, Age <dbl>, Outcome <dbl>
colnames(test)
## [1] "Pregnancies"              "Glucose"                 
## [3] "BloodPressure"            "SkinThickness"           
## [5] "Insulin"                  "BMI"                     
## [7] "DiabetesPedigreeFunction" "Age"                     
## [9] "Outcome"
heatmaply_cor(x = cor(test), xlab = "Features", 
              ylab = "Features", k_col = 2, k_row = 2)
## Warning in doTryCatch(return(expr), name, parentenv, handler): unable to load shared object '/Library/Frameworks/R.framework/Resources/modules//R_X11.so':
##   dlopen(/Library/Frameworks/R.framework/Resources/modules//R_X11.so, 0x0006): Library not loaded: /opt/X11/lib/libSM.6.dylib
##   Referenced from: <B3716E5A-BF4D-3CA3-B8EB-89643DB72A04> /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/modules/R_X11.so
##   Reason: tried: '/opt/X11/lib/libSM.6.dylib' (no such file), '/System/Volumes/Preboot/Cryptexes/OS/opt/X11/lib/libSM.6.dylib' (no such file), '/opt/X11/lib/libSM.6.dylib' (no such file), '/Library/Frameworks/R.framework/Resources/lib/libSM.6.dylib' (no such file), '/Library/Java/JavaVirtualMachines/jdk-11.0.18+10/Contents/Home/lib/server/libSM.6.dylib' (no such file)
cor_matrix <- cor(test)

print(cor_matrix)
##                          Pregnancies    Glucose BloodPressure SkinThickness
## Pregnancies               1.00000000 0.12945867    0.14128198   -0.08167177
## Glucose                   0.12945867 1.00000000    0.15258959    0.05732789
## BloodPressure             0.14128198 0.15258959    1.00000000    0.20737054
## SkinThickness            -0.08167177 0.05732789    0.20737054    1.00000000
## Insulin                  -0.07353461 0.33135711    0.08893338    0.43678257
## BMI                       0.01768309 0.22107107    0.28180529    0.39257320
## DiabetesPedigreeFunction -0.03352267 0.13733730    0.04126495    0.18392757
## Age                       0.54434123 0.26351432    0.23952795   -0.11397026
## Outcome                   0.22189815 0.46658140    0.06506836    0.07475223
##                              Insulin        BMI DiabetesPedigreeFunction
## Pregnancies              -0.07353461 0.01768309              -0.03352267
## Glucose                   0.33135711 0.22107107               0.13733730
## BloodPressure             0.08893338 0.28180529               0.04126495
## SkinThickness             0.43678257 0.39257320               0.18392757
## Insulin                   1.00000000 0.19785906               0.18507093
## BMI                       0.19785906 1.00000000               0.14064695
## DiabetesPedigreeFunction  0.18507093 0.14064695               1.00000000
## Age                      -0.04216295 0.03624187               0.03356131
## Outcome                   0.13054795 0.29269466               0.17384407
##                                  Age    Outcome
## Pregnancies               0.54434123 0.22189815
## Glucose                   0.26351432 0.46658140
## BloodPressure             0.23952795 0.06506836
## SkinThickness            -0.11397026 0.07475223
## Insulin                  -0.04216295 0.13054795
## BMI                       0.03624187 0.29269466
## DiabetesPedigreeFunction  0.03356131 0.17384407
## Age                       1.00000000 0.23835598
## Outcome                   0.23835598 1.00000000
summary(test)
##   Pregnancies        Glucose      BloodPressure    SkinThickness  
##  Min.   : 0.000   Min.   :  0.0   Min.   :  0.00   Min.   : 0.00  
##  1st Qu.: 1.000   1st Qu.: 99.0   1st Qu.: 62.00   1st Qu.: 0.00  
##  Median : 3.000   Median :117.0   Median : 72.00   Median :23.00  
##  Mean   : 3.845   Mean   :120.9   Mean   : 69.11   Mean   :20.54  
##  3rd Qu.: 6.000   3rd Qu.:140.2   3rd Qu.: 80.00   3rd Qu.:32.00  
##  Max.   :17.000   Max.   :199.0   Max.   :122.00   Max.   :99.00  
##     Insulin           BMI        DiabetesPedigreeFunction      Age       
##  Min.   :  0.0   Min.   : 0.00   Min.   :0.0780           Min.   :21.00  
##  1st Qu.:  0.0   1st Qu.:27.30   1st Qu.:0.2437           1st Qu.:24.00  
##  Median : 30.5   Median :32.00   Median :0.3725           Median :29.00  
##  Mean   : 79.8   Mean   :31.99   Mean   :0.4719           Mean   :33.24  
##  3rd Qu.:127.2   3rd Qu.:36.60   3rd Qu.:0.6262           3rd Qu.:41.00  
##  Max.   :846.0   Max.   :67.10   Max.   :2.4200           Max.   :81.00  
##     Outcome     
##  Min.   :0.000  
##  1st Qu.:0.000  
##  Median :0.000  
##  Mean   :0.349  
##  3rd Qu.:1.000  
##  Max.   :1.000
ggplot(test, aes(test$Outcome, fill = test$Outcome, color = Outcome)) + 
  geom_bar() +
  labs(title = "Diabetes Outcome", x = "Diabetes") +
  geom_text(stat = "count", aes(label = ..count..), vjust = -0.5) +
  theme(plot.title = element_text(hjust = 0.5))
## Warning: Use of `test$Outcome` is discouraged.
## ℹ Use `Outcome` instead.
## Use of `test$Outcome` is discouraged.
## ℹ Use `Outcome` instead.
## Warning: The dot-dot notation (`..count..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(count)` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: Use of `test$Outcome` is discouraged.
## ℹ Use `Outcome` instead.
## Use of `test$Outcome` is discouraged.
## ℹ Use `Outcome` instead.
## Warning: The following aesthetics were dropped during statistical transformation: fill,
## colour
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?
## The following aesthetics were dropped during statistical transformation: fill,
## colour
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

df <- test %>%
  gather(key = "variable", value = "value")

# Plot the distribution of all columns in one graph
ggplot(df, aes(x = value, fill = variable)) +
  geom_density(alpha = 0.5) +
  labs(title = "Distribution of All Columns",
       x = "Value",
       y = "Density") +
  theme_minimal() + 
  ylim(c(0, 0.2)) +
  xlim(c(0, 250)) 
## Warning: Removed 56 rows containing non-finite values (`stat_density()`).

# Calculate skewness for each column
skewness_values <- sapply(test, skewness)

# Print the skewness values
print(skewness_values)
##              Pregnancies                  Glucose            BloodPressure 
##                0.8981549                0.1730754               -1.8364126 
##            SkinThickness                  Insulin                      BMI 
##                0.1089456                2.2633826               -0.4273073 
## DiabetesPedigreeFunction                      Age                  Outcome 
##                1.9124179                1.1251880                0.6325383
# Separate glucose levels for diabetic and non-diabetic individuals
blood_pressure_age_lt_30 <- test$BloodPressure[test$Age <= 30]
blood_pressure_age_gt_30 <- test$BloodPressure[test$Age > 30]
t_test_result <- t.test(blood_pressure_age_lt_30, blood_pressure_age_gt_30)
print(t_test_result)
## 
##  Welch Two Sample t-test
## 
## data:  blood_pressure_age_lt_30 and blood_pressure_age_gt_30
## t = -6.0637, df = 755.07, p-value = 2.101e-09
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
##  -10.953401  -5.595698
## sample estimates:
## mean of x mean of y 
##  65.32374  73.59829
cor_test_result <- cor.test(test$Age, test$BloodPressure)
print(cor_test_result)
## 
##  Pearson's product-moment correlation
## 
## data:  test$Age and test$BloodPressure
## t = 6.8281, df = 766, p-value = 1.752e-11
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
##  0.1716931 0.3051022
## sample estimates:
##       cor 
## 0.2395279
test1 <- subset(test, BloodPressure != 0)


# Scatter plot with color based on another variable
ggplot(test1, aes(x = Age, y = BloodPressure, color = Outcome)) +
  geom_point() +
  labs(title = "Scatter Plot of BloodPressure vs. Age",
       x = "Age",
       y = "BloodPressure",
       color = "Outcome") +
       geom_smooth(method = "lm", se = TRUE, aes(group = 1)) +
       annotate("text", x = Inf, y = Inf, hjust = 1, vjust = 1,
           label = paste("Regression Coefficient: ",
                         round(coef(lm(Age ~ BloodPressure, data = data))[2], 4)),
           color = "red")
## `geom_smooth()` using formula = 'y ~ x'
## Warning: The following aesthetics were dropped during statistical transformation: colour
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
##   the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
##   variable into a factor?

  theme_minimal()
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